Understanding and Improving Drilled-Down Information Extraction from Online Data Visualizations for Screen-Reader Users

Abstract

Inaccessible online data visualizations can significantly disenfranchise screen-reader users from accessing critical online information. Current accessibility measures, such as adding alternative text to visualizations, only provide a high-level overview of data, limiting screen-reader users from exploring data visualizations in depth. In this work, we build on prior research to develop taxonomies of information sought by screen-reader users to interact with online data visualizations granularly through role-based and longitudinal studies with screen-reader users. Utilizing these taxonomies, we extended the functionality of VoxLens-an open-source multi-modal system that improves the accessibility of data visualizations-by supporting drilled-down information extraction. We assessed the performance of our VoxLens enhancements through task-based user studies with 10 screen-reader and 10 non-screen-reader users. Our enhancements "closed the gap"between the two groups by enabling screen-reader users to extract information with approximately the same accuracy as non-screen-reader users, reducing interaction time by 22% in the process.